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  • 标题:Achieving natural behavior in a robot using neurally inspired hierarchical perceptual control
  • 本地全文:下载
  • 作者:Joseph W. Barter ; Henry H. Yin
  • 期刊名称:iScience
  • 印刷版ISSN:2589-0042
  • 出版年度:2021
  • 卷号:24
  • 期号:9
  • 页码:1-18
  • DOI:10.1016/j.isci.2021.102948
  • 语种:English
  • 出版社:Elsevier
  • 摘要:SummaryTerrestrial locomotion presents tremendous computational challenges on account of the enormous degrees of freedom in legged animals, and complex, unpredictable properties of natural environments, including the body and its effectors, yet the nervous system can achieve locomotion with ease. Here we introduce a quadrupedal robot that is capable of posture control and goal-directed locomotion across uneven terrain. The control architecture is a hierarchical network of simple negative feedback control systems inspired by the organization of the vertebrate nervous system. This robot is capable of robust posture control and locomotion in novel environments with unpredictable disturbances. Unlike current robots, our robot does not use internal inverse and forward models, nor does it require any training in order to perform successfully in novel environments.Graphical abstractDisplay OmittedHighlights•Inspired by a neural hierarchy with control of input at each level•Higher level specifies reference state for lower level•Successful posture control and locomotion despite unpredictable disturbances•No need for training or computation of inverse or forward kinematicsBiological sciences; Neuroscience; Sensory neuroscience; Robotics
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